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demo_adult.m
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demo_adult.m
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clear
clc
adult_train_R;
adult_test_R;
% do normalization for each feature
data=[data1;data2];
N=size(data1,1);
data=data(:,2:end);
dataX=data(:,1:end-1);
mean_X=mean(dataX,1);
dataX=dataX-repmat(mean_X,size(dataX,1),1);
norm_X=sum(dataX.^2,1);
norm_X=sqrt(norm_X);
norm_X=repmat(norm_X,size(dataX,1),1);
dataX=dataX./norm_X;
dataY=data(:,end);
trainX=dataX(1:N,:);
trainY=dataY(1:N,:);
testX=dataX(N+1:end,:);
testY=dataY(N+1:end,:);
% for some data where the training-testing partitioning is available, the parameter is tuned on the training set and evaluated on the test data.
adult_conxuntos; %divide the training data into two parts to do paramater tuning.
trainX_temp=trainX(index1,:);
trainY_temp=trainY(index1);
testX_temp=trainX(index2,:);
testY_temp=trainY(index2);
MAX_acc=zeros(4,1);
Best_N=zeros(4,1);
Best_C=zeros(4,1);
Best_S=zeros(4,1);
S=-5:0.5:5;
for s=1:numel(S)
for N=3:20:203
for C=-5:14
Scale=2^S(s);
option1.N=N;
option1.C=2^C;
option1.Scale=Scale;
option1.Scalemode=3;
option1.bias=0;
option1.link=0;
option2.N=N;
option2.C=2^C;
option2.Scale=Scale;
option2.Scalemode=3;
option2.bias=1;
option2.link=0;
option3.N=N;
option3.C=2^C;
option3.Scale=Scale;
option3.Scalemode=3;
option3.bias=0;
option3.link=1;
option4.N=N;
option4.C=2^C;
option4.Scale=Scale;
option4.Scalemode=3;
option4.bias=1;
option4.link=1;
[train_accuracy1,test_accuracy1]=RVFL_train_val(trainX_temp,trainY_temp,testX_temp,testY_temp,option1);
[train_accuracy2,test_accuracy2]=RVFL_train_val(trainX_temp,trainY_temp,testX_temp,testY_temp,option2);
[train_accuracy3,test_accuracy3]=RVFL_train_val(trainX_temp,trainY_temp,testX_temp,testY_temp,option3);
[train_accuracy4,test_accuracy4]=RVFL_train_val(trainX_temp,trainY_temp,testX_temp,testY_temp,option4);
if test_accuracy1>MAX_acc(1);% paramater tuning: we prefer the parameter which lead to better accuracy on the test data.
MAX_acc(1)=test_accuracy1;
Best_N(1)=N;
Best_C(1)=C;
Best_S(1)=Scale;
end
if test_accuracy2>MAX_acc(2);% paramater tuning: we prefer the parameter which lead to better accuracy on the test data.
MAX_acc(2)=test_accuracy2;
Best_N(2)=N;
Best_C(2)=C;
Best_S(2)=Scale;
end
if test_accuracy3>MAX_acc(3);% paramater tuning: we prefer the parameter which lead to better accuracy on the test data.
MAX_acc(3)=test_accuracy3;
Best_N(3)=N;
Best_C(3)=C;
Best_S(3)=Scale;
end
if test_accuracy4>MAX_acc(4);% paramater tuning: we prefer the parameter which lead to better accuracy on the test data.
MAX_acc(4)=test_accuracy4;
Best_N(4)=N;
Best_C(4)=C;
Best_S(4)=Scale;
end
end
end
end
for i=1:4
option1.N=Best_N(1);
option1.C=2^Best_C(1);
option1.Scale=Best_S(1);
option1.Scalemode=3;
option1.bias=0;
option1.link=0;
option2.N=Best_N(2);
option2.C=2^Best_C(2);
option2.Scale=Best_S(2);
option2.Scalemode=3;
option2.bias=1;
option2.link=0;
option3.N=Best_N(3);
option3.C=2^Best_C(3);
option3.Scale=Best_S(3);
option3.Scalemode=3;
option3.bias=0;
option3.link=1;
option4.N=Best_N(4);
option4.C=2^Best_C(4);
option4.Scale=Best_S(4);
option4.Scalemode=3;
option4.bias=1;
option4.link=1;
[train_accuracy1,ACC_CV(1,i)]=RVFL_train_val(trainX,trainY,testX,testY,option1); % ACC_CV each row is the accuracy for one RVFL configuration. Each column is a single trial for cross-validation.
[train_accuracy2,ACC_CV(2,i)]=RVFL_train_val(trainX,trainY,testX,testY,option2);
[train_accuracy3,ACC_CV(3,i)]=RVFL_train_val(trainX,trainY,testX,testY,option3);
[train_accuracy4,ACC_CV(4,i)]=RVFL_train_val(trainX,trainY,testX,testY,option4);
end
mean(ACC_CV,2)